Dense retrieval can return different evidence when a query is paraphrased, even when the meaning stays the same. This means that linguistic variation can create uncertainty in the evidence given to a retrieval-augmented generation (RAG) system. We study this problem across HotpotQA, FEVER, and SQuAD 2.0 using five dense encoders spanning 22M to 8B parameters and multiple model families. Paraphrase Jaccard distance remains 32 to 50 percent, showing that retrieval instability persists with stronger and alternative encoders. We then ask how much this problem can be reduced at inference time. Multi-source consensus over paraphrased queries and different retrievers improves retrieval coverage. Fixed source-type weights can help in harder settings. However, adapting those weights to each query using retrieval stability changes Coverage@20 by at most ±0.3 percentage points per setting. Retrieval stability is more useful as a label-free routing signal: on HotpotQA/MiniLM, routing only unstable queries through the stability-conditioned consensus configuration reduces retrieval calls by 47 percent for a 0.6 percentage-point Coverage@20 cost compared with always using that configuration. Overall, paraphrase-induced retrieval instability is a persistent source of uncertainty in retrieved evidence, and the query-adaptive stability component provides only a small marginal refinement.

We present a non-visual baseline for GoldenViewVQA, a benchmark for multi-view visual question answering with evidence-source identification. The baseline reaches a joint accuracy of 0.7119 on the hidden test split and uses no visual input. Its view prediction is the fixed label CAM_FRONT, which reaches 0.7797 view accuracy on the test split, while Qwen3-4B predicts the answer from the question, answer options, and non-visual metadata with 0.8475 answer accuracy. These results show that the current benchmark permits substantial performance without image access. We analyze the supporting-view prior and performance across question groups, and discuss benchmark changes that could reduce reliance on non-visual shortcuts. We present this run as a strong non-visual baseline, not as evidence that vision is unnecessary for driving question answering. Code: https://github.com/aravind-mandiga/groundlm-goldenviewvqa.